{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分类数据\n",
    "pandas的分类类型能够提高性能和内存的使用率。\n",
    "# 问题\n",
    "表中的一列通常会有重复的包含不同值的小集合的情况。我们已经学过了unique和value_counts，它们可以从数组提取出不同的值，并分别计算频率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "      <th>totals</th>\n",
       "      <th>totalAssets</th>\n",
       "      <th>liquidAssets</th>\n",
       "      <th>fixedAssets</th>\n",
       "      <th>reserved</th>\n",
       "      <th>...</th>\n",
       "      <th>bvps</th>\n",
       "      <th>pb</th>\n",
       "      <th>timeToMarket</th>\n",
       "      <th>undp</th>\n",
       "      <th>perundp</th>\n",
       "      <th>rev</th>\n",
       "      <th>profit</th>\n",
       "      <th>gpr</th>\n",
       "      <th>npr</th>\n",
       "      <th>holders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002938</th>\n",
       "      <td>N鹏鼎</td>\n",
       "      <td>元器件</td>\n",
       "      <td>深圳</td>\n",
       "      <td>62.15</td>\n",
       "      <td>2.31</td>\n",
       "      <td>23.11</td>\n",
       "      <td>2032557.75</td>\n",
       "      <td>1040264.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.69</td>\n",
       "      <td>4.07</td>\n",
       "      <td>20180918</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.54</td>\n",
       "      <td>408095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>升达林业</td>\n",
       "      <td>供气供热</td>\n",
       "      <td>四川</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.24</td>\n",
       "      <td>7.52</td>\n",
       "      <td>284906.13</td>\n",
       "      <td>171517.98</td>\n",
       "      <td>79040.80</td>\n",
       "      <td>71885.80</td>\n",
       "      <td>...</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.90</td>\n",
       "      <td>20080716</td>\n",
       "      <td>13266.97</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-14.18</td>\n",
       "      <td>-466.68</td>\n",
       "      <td>12.10</td>\n",
       "      <td>-6.56</td>\n",
       "      <td>37927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>恒康医疗</td>\n",
       "      <td>中成药</td>\n",
       "      <td>甘肃</td>\n",
       "      <td>46.30</td>\n",
       "      <td>18.65</td>\n",
       "      <td>18.65</td>\n",
       "      <td>1004764.88</td>\n",
       "      <td>319343.19</td>\n",
       "      <td>203795.13</td>\n",
       "      <td>108219.77</td>\n",
       "      <td>...</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.57</td>\n",
       "      <td>20080306</td>\n",
       "      <td>118436.72</td>\n",
       "      <td>0.63</td>\n",
       "      <td>67.86</td>\n",
       "      <td>-56.30</td>\n",
       "      <td>31.11</td>\n",
       "      <td>3.88</td>\n",
       "      <td>24831.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>红宇新材</td>\n",
       "      <td>钢加工</td>\n",
       "      <td>湖南</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4.41</td>\n",
       "      <td>94718.11</td>\n",
       "      <td>45858.59</td>\n",
       "      <td>20612.00</td>\n",
       "      <td>24669.55</td>\n",
       "      <td>...</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.92</td>\n",
       "      <td>20120801</td>\n",
       "      <td>1418.97</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-39.70</td>\n",
       "      <td>-95.66</td>\n",
       "      <td>25.88</td>\n",
       "      <td>-4.96</td>\n",
       "      <td>54901.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>迪威迅</td>\n",
       "      <td>通信设备</td>\n",
       "      <td>深圳</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>118868.60</td>\n",
       "      <td>65652.96</td>\n",
       "      <td>6827.10</td>\n",
       "      <td>31287.58</td>\n",
       "      <td>...</td>\n",
       "      <td>2.32</td>\n",
       "      <td>2.31</td>\n",
       "      <td>20110125</td>\n",
       "      <td>6951.87</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-34.11</td>\n",
       "      <td>-1324.05</td>\n",
       "      <td>21.03</td>\n",
       "      <td>-9.96</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        name industry area     pe  outstanding  totals  totalAssets  \\\n",
       "code                                                                  \n",
       "002938   N鹏鼎      元器件   深圳  62.15         2.31   23.11   2032557.75   \n",
       "002259  升达林业     供气供热   四川   0.00         7.24    7.52    284906.13   \n",
       "002219  恒康医疗      中成药   甘肃  46.30        18.65   18.65   1004764.88   \n",
       "300345  红宇新材      钢加工   湖南   0.00         3.27    4.41     94718.11   \n",
       "300167   迪威迅     通信设备   深圳   0.00         3.00    3.00    118868.60   \n",
       "\n",
       "        liquidAssets  fixedAssets   reserved    ...     bvps    pb  \\\n",
       "code                                            ...                  \n",
       "002938    1040264.13         0.00       0.00    ...     5.69  4.07   \n",
       "002259     171517.98     79040.80   71885.80    ...     2.18  1.90   \n",
       "002219     319343.19    203795.13  108219.77    ...     2.28  1.57   \n",
       "300345      45858.59     20612.00   24669.55    ...     1.64  2.92   \n",
       "300167      65652.96      6827.10   31287.58    ...     2.32  2.31   \n",
       "\n",
       "        timeToMarket       undp  perundp    rev   profit    gpr   npr  \\\n",
       "code                                                                    \n",
       "002938      20180918       0.00     0.00   0.00     0.00   0.00  4.54   \n",
       "002259      20080716   13266.97     0.18 -14.18  -466.68  12.10 -6.56   \n",
       "002219      20080306  118436.72     0.63  67.86   -56.30  31.11  3.88   \n",
       "300345      20120801    1418.97     0.03 -39.70   -95.66  25.88 -4.96   \n",
       "300167      20110125    6951.87     0.23 -34.11 -1324.05  21.03 -9.96   \n",
       "\n",
       "         holders  \n",
       "code              \n",
       "002938  408095.0  \n",
       "002259   37927.0  \n",
       "002219   24831.0  \n",
       "300345   54901.0  \n",
       "300167   20000.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=ts.get_stock_basics()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002938</th>\n",
       "      <td>元器件</td>\n",
       "      <td>深圳</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>供气供热</td>\n",
       "      <td>四川</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>中成药</td>\n",
       "      <td>甘肃</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>钢加工</td>\n",
       "      <td>湖南</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>通信设备</td>\n",
       "      <td>深圳</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       industry area\n",
       "code                \n",
       "002938      元器件   深圳\n",
       "002259     供气供热   四川\n",
       "002219      中成药   甘肃\n",
       "300345      钢加工   湖南\n",
       "300167     通信设备   深圳"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock=df.loc[:,['industry','area']]\n",
    "stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['深圳', '四川', '甘肃', '湖南', '安徽', '浙江', '上海', '广西', '重庆', '江苏', '宁夏',\n",
       "       '山东', '陕西', '湖北', '广东', '福建', '青海', '北京', '西藏', '贵州', '新疆', '河南',\n",
       "       '山西', '江西', '辽宁', '云南', '黑龙江', '吉林', '海南', '河北', '天津', '内蒙'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.area.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "浙江     429\n",
       "江苏     398\n",
       "北京     314\n",
       "广东     304\n",
       "上海     284\n",
       "深圳     282\n",
       "山东     194\n",
       "福建     131\n",
       "四川     120\n",
       "安徽     104\n",
       "湖南     103\n",
       "湖北     100\n",
       "河南      79\n",
       "辽宁      72\n",
       "河北      56\n",
       "新疆      53\n",
       "天津      50\n",
       "重庆      48\n",
       "陕西      47\n",
       "江西      41\n",
       "吉林      41\n",
       "山西      38\n",
       "广西      37\n",
       "黑龙江     37\n",
       "甘肃      33\n",
       "云南      33\n",
       "海南      31\n",
       "贵州      29\n",
       "内蒙      25\n",
       "西藏      17\n",
       "宁夏      13\n",
       "青海      12\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.area.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "许多数据系统（数据仓库、统计计算或其它应用）都发展出了特定的表征重复值的方法，以进行高效的存储和计算。在数据仓库中，最好的方法是使用所谓的包含不同值得维表(Dimension Table)，将主要的参数存储为引用维表整数键："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    0\n",
       " 1    1\n",
       " 2    0\n",
       " 3    0\n",
       " 4    0\n",
       " 5    1\n",
       " 6    0\n",
       " 7    0\n",
       " dtype: int64, 0    horse\n",
       " 1    sheep\n",
       " dtype: object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v=pd.Series([0,1,0,0]*2)\n",
    "d=pd.Series(['horse','sheep'])\n",
    "v,d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    horse\n",
       "1    sheep\n",
       "0    horse\n",
       "0    horse\n",
       "0    horse\n",
       "1    sheep\n",
       "0    horse\n",
       "0    horse\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.take(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这种用整数表示的方法称为分类或字典编码表示法。不同值得数组称为分类、字典或数据级。表示分类的整数值称为分类编码或简单地称为编码。\n",
    "分类表示可以在进行分析时大大的提高性能。你也可以在保持编码不变的情况下，对分类进行转换。一些相对简单的转变例子包括：\n",
    "* 重命名分类。\n",
    "* 加入一个新的分类，不改变已经存在的分类的顺序或位置。\n",
    "\n",
    "\n",
    "# pandas的分类类型\n",
    "pandas有一个特殊的分类类型，用于保存使用整数分类表示法的数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code\n",
       "002938    深圳\n",
       "002259    四川\n",
       "002219    甘肃\n",
       "300345    湖南\n",
       "300167    深圳\n",
       "Name: area, dtype: category\n",
       "Categories (32, object): [上海, 云南, 内蒙, 北京, ..., 重庆, 陕西, 青海, 黑龙江]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area=df.area.astype('category')\n",
    "area.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.arrays.categorical.Categorical"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(area.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=area.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['上海', '云南', '内蒙', '北京', '吉林', '四川', '天津', '宁夏', '安徽', '山东', '山西', '广东',\n",
       "        '广西', '新疆', '江苏', '江西', '河北', '河南', '浙江', '海南', '深圳', '湖北', '湖南', '甘肃',\n",
       "        '福建', '西藏', '贵州', '辽宁', '重庆', '陕西', '青海', '黑龙江'],\n",
       "       dtype='object'), array([20,  5, 23, ..., 18, 17, 24], dtype=int8))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.categories,c.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将DF的列转化为分类类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002938</th>\n",
       "      <td>元器件</td>\n",
       "      <td>深圳</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>供气供热</td>\n",
       "      <td>四川</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>中成药</td>\n",
       "      <td>甘肃</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>钢加工</td>\n",
       "      <td>湖南</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>通信设备</td>\n",
       "      <td>深圳</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       industry area\n",
       "code                \n",
       "002938      元器件   深圳\n",
       "002259     供气供热   四川\n",
       "002219      中成药   甘肃\n",
       "300345      钢加工   湖南\n",
       "300167     通信设备   深圳"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat=stock.assign(area=stock.area.astype('category'))\n",
    "cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code\n",
       "002938    深圳\n",
       "002259    四川\n",
       "002219    甘肃\n",
       "300345    湖南\n",
       "300167    深圳\n",
       "Name: area, dtype: category\n",
       "Categories (32, object): [上海, 云南, 内蒙, 北京, ..., 重庆, 陕西, 青海, 黑龙江]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.area.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用分类进行计算\n",
    "使用pandas.qcut面元函数。它会返回category对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=pd.qcut(df.pe,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code\n",
       "002938    (37.607, 67.027]\n",
       "002259      (-0.001, 9.73]\n",
       "002219    (37.607, 67.027]\n",
       "300345      (-0.001, 9.73]\n",
       "300167      (-0.001, 9.73]\n",
       "Name: pe, dtype: category\n",
       "Categories (6, interval[float64]): [(-0.001, 9.73] < (9.73, 17.787] < (17.787, 25.77] < (25.77, 37.607] < (37.607, 67.027] < (67.027, 8203.07]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code\n",
       "002938    e\n",
       "002259    a\n",
       "002219    e\n",
       "300345    a\n",
       "300167    a\n",
       "Name: pe, dtype: category\n",
       "Categories (6, object): [a < b < c < d < e < f]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins=pd.qcut(df.pe,6,labels=list('abcdef'))\n",
    "bins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['a', 'b', 'c', 'd', 'e', 'f'], dtype='object'),\n",
       " array([4, 0, 4, ..., 0, 0, 0], dtype=int8))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins.values.categories,bins.values.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加上标签的面元分类不包含数据面元边界的信息，因此可以使用groupby提取一些汇总信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pe</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>593</td>\n",
       "      <td>2.664250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>592</td>\n",
       "      <td>14.162584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>593</td>\n",
       "      <td>21.694401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>592</td>\n",
       "      <td>31.164122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>592</td>\n",
       "      <td>49.758514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>593</td>\n",
       "      <td>259.201282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count        mean\n",
       "pe                   \n",
       "a     593    2.664250\n",
       "b     592   14.162584\n",
       "c     593   21.694401\n",
       "d     592   31.164122\n",
       "e     592   49.758514\n",
       "f     593  259.201282"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pe.groupby(bins).agg(['count','mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用分类提高性能\n",
    "如果你是在一个特定数据集上做大量分析，将其转换为分类可以极大地提高效率。DataFrame列的分类使用的内存通常少的多。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat=df.area.astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197371, 220720)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.memory_usage(),df.area.memory_usage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 为建模创建虚拟变量\n",
    "当你使用统计或机器学习工具时，通常会将分类数据转换为虚拟变量，也称为one-hot编码。这包括创建一个不同类别的列的DataFrame；这些列包含给定分类的1，其它为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>上海</th>\n",
       "      <th>云南</th>\n",
       "      <th>内蒙</th>\n",
       "      <th>北京</th>\n",
       "      <th>吉林</th>\n",
       "      <th>四川</th>\n",
       "      <th>天津</th>\n",
       "      <th>宁夏</th>\n",
       "      <th>安徽</th>\n",
       "      <th>山东</th>\n",
       "      <th>...</th>\n",
       "      <th>湖南</th>\n",
       "      <th>甘肃</th>\n",
       "      <th>福建</th>\n",
       "      <th>西藏</th>\n",
       "      <th>贵州</th>\n",
       "      <th>辽宁</th>\n",
       "      <th>重庆</th>\n",
       "      <th>陕西</th>\n",
       "      <th>青海</th>\n",
       "      <th>黑龙江</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002938</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        上海  云南  内蒙  北京  吉林  四川  天津  宁夏  安徽  山东 ...   湖南  甘肃  福建  西藏  贵州  辽宁  \\\n",
       "code                                           ...                            \n",
       "002938   0   0   0   0   0   0   0   0   0   0 ...    0   0   0   0   0   0   \n",
       "002259   0   0   0   0   0   1   0   0   0   0 ...    0   0   0   0   0   0   \n",
       "002219   0   0   0   0   0   0   0   0   0   0 ...    0   1   0   0   0   0   \n",
       "300345   0   0   0   0   0   0   0   0   0   0 ...    1   0   0   0   0   0   \n",
       "300167   0   0   0   0   0   0   0   0   0   0 ...    0   0   0   0   0   0   \n",
       "\n",
       "        重庆  陕西  青海  黑龙江  \n",
       "code                     \n",
       "002938   0   0   0    0  \n",
       "002259   0   0   0    0  \n",
       "002219   0   0   0    0  \n",
       "300345   0   0   0    0  \n",
       "300167   0   0   0    0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df.area.astype('category')).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy高级应用,分组转换和“解封”GroupBy\n",
    "我们在分组操作中学习了apply方法，进行转换。还有另一个transform方法，它与apply很像，但是对使用的函数有一定限制：\n",
    "\n",
    "* 它可以产生向分组形状广播标量值\n",
    "* 它可以产生一个和输入组形状相同的对象\n",
    "* 它不能修改输入\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>47.486514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>121.247576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙</th>\n",
       "      <td>21.831200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>72.518662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>53.978049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pe\n",
       "area            \n",
       "上海     47.486514\n",
       "云南    121.247576\n",
       "内蒙     21.831200\n",
       "北京     72.518662\n",
       "吉林     53.978049"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g=df.loc[:,['area','pe']].groupby('area')\n",
    "g.mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设我们想产生一个和df.loc[:,['area','pe']]形状相同的Series，但值替换为按键分组的平均值。我们可以传递函数lambda x: x.mean()进行转换："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002938</th>\n",
       "      <td>82.186418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>51.206917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>27.800303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>77.246408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>82.186418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pe\n",
       "code             \n",
       "002938  82.186418\n",
       "002259  51.206917\n",
       "002219  27.800303\n",
       "300345  77.246408\n",
       "300167  82.186418"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform(lambda x:x.mean()).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002938</th>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pe\n",
       "code       \n",
       "002938  282\n",
       "002259  120\n",
       "002219   33\n",
       "300345  103\n",
       "300167  282"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform('count').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以计算每个分组的降序排名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002938</th>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>94.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>271.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pe\n",
       "code         \n",
       "002938   61.0\n",
       "002259  114.0\n",
       "002219    8.0\n",
       "300345   94.5\n",
       "300167  271.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform(lambda x:x.rank(ascending=False)).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看一个由简单聚合构造的分组转化函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002938</th>\n",
       "      <td>-0.084609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>-0.717331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>0.564356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>-0.242314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>-0.347052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pe\n",
       "code            \n",
       "002938 -0.084609\n",
       "002259 -0.717331\n",
       "002219  0.564356\n",
       "300345 -0.242314\n",
       "300167 -0.347052"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform(lambda x:(x-x.mean())/x.std()).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以用tansform或者apply可以获得等价结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>002938</th>\n",
       "      <td>-0.084609</td>\n",
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       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>-0.717331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>0.564356</td>\n",
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       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>-0.242314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>-0.347052</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pe\n",
       "code            \n",
       "002938 -0.084609\n",
       "002259 -0.717331\n",
       "002219  0.564356\n",
       "300345 -0.242314\n",
       "300167 -0.347052"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.apply(lambda x:(x-x.mean())/x.std()).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "内置的聚合函数，比如mean或sum，通常比apply函数快，也比transform快。这允许我们进行一个所谓的解封（unwrapped）分组操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002938</th>\n",
       "      <td>-0.084609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002259</th>\n",
       "      <td>-0.717331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002219</th>\n",
       "      <td>0.564356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300345</th>\n",
       "      <td>-0.242314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300167</th>\n",
       "      <td>-0.347052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pe\n",
       "code            \n",
       "002938 -0.084609\n",
       "002259 -0.717331\n",
       "002219  0.564356\n",
       "300345 -0.242314\n",
       "300167 -0.347052"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df.loc[:,['pe']]-g.transform('mean'))/g.transform('std')).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 股票行业地域热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">专用机械</th>\n",
       "      <th>上海</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pe\n",
       "industry area    \n",
       "专用机械     上海    10\n",
       "         北京     7\n",
       "         四川     6\n",
       "         天津     1\n",
       "         安徽     5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_style('whitegrid',{'font.sans-serif':['simhei','Arial']})\n",
    "a=df.loc[:,['industry','area','pe']]\n",
    "b=a.groupby(['industry','area']).count()\n",
    "b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>专用机械</td>\n",
       "      <td>上海</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>专用机械</td>\n",
       "      <td>北京</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>专用机械</td>\n",
       "      <td>四川</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>专用机械</td>\n",
       "      <td>天津</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>专用机械</td>\n",
       "      <td>安徽</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  industry area  pe\n",
       "0     专用机械   上海  10\n",
       "1     专用机械   北京   7\n",
       "2     专用机械   四川   6\n",
       "3     专用机械   天津   1\n",
       "4     专用机械   安徽   5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=b.reset_index()\n",
    "c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>area</th>\n",
       "      <th>上海</th>\n",
       "      <th>云南</th>\n",
       "      <th>内蒙</th>\n",
       "      <th>北京</th>\n",
       "      <th>吉林</th>\n",
       "      <th>四川</th>\n",
       "      <th>天津</th>\n",
       "      <th>宁夏</th>\n",
       "      <th>安徽</th>\n",
       "      <th>山东</th>\n",
       "      <th>...</th>\n",
       "      <th>湖南</th>\n",
       "      <th>甘肃</th>\n",
       "      <th>福建</th>\n",
       "      <th>西藏</th>\n",
       "      <th>贵州</th>\n",
       "      <th>辽宁</th>\n",
       "      <th>重庆</th>\n",
       "      <th>陕西</th>\n",
       "      <th>青海</th>\n",
       "      <th>黑龙江</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>专用机械</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>仓储物流</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "area        上海   云南   内蒙    北京   吉林   四川   天津  宁夏   安徽   山东 ...    湖南   甘肃  \\\n",
       "industry                                                    ...              \n",
       "专用机械      10.0  NaN  NaN   7.0  NaN  6.0  1.0 NaN  5.0  4.0 ...   NaN  1.0   \n",
       "中成药        3.0  3.0  1.0   2.0  5.0  2.0  3.0 NaN  NaN  3.0 ...   5.0  3.0   \n",
       "乳制品        2.0  NaN  1.0   1.0  NaN  NaN  NaN NaN  NaN  NaN ...   NaN  1.0   \n",
       "互联网        5.0  NaN  NaN  11.0  NaN  1.0  NaN NaN  1.0  3.0 ...   2.0  NaN   \n",
       "仓储物流       6.0  NaN  NaN   3.0  NaN  2.0  1.0 NaN  NaN  1.0 ...   NaN  NaN   \n",
       "\n",
       "area       福建   西藏   贵州   辽宁   重庆   陕西   青海  黑龙江  \n",
       "industry                                          \n",
       "专用机械      4.0  NaN  NaN  8.0  NaN  1.0  NaN  NaN  \n",
       "中成药       1.0  3.0  4.0  NaN  1.0  3.0  1.0  2.0  \n",
       "乳制品       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "互联网       5.0  NaN  NaN  2.0  1.0  NaN  1.0  NaN  \n",
       "仓储物流      1.0  NaN  NaN  1.0  NaN  NaN  NaN  1.0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=c.pivot('industry','area','pe')\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>area</th>\n",
       "      <th>上海</th>\n",
       "      <th>云南</th>\n",
       "      <th>内蒙</th>\n",
       "      <th>北京</th>\n",
       "      <th>吉林</th>\n",
       "      <th>四川</th>\n",
       "      <th>天津</th>\n",
       "      <th>宁夏</th>\n",
       "      <th>安徽</th>\n",
       "      <th>山东</th>\n",
       "      <th>...</th>\n",
       "      <th>湖南</th>\n",
       "      <th>甘肃</th>\n",
       "      <th>福建</th>\n",
       "      <th>西藏</th>\n",
       "      <th>贵州</th>\n",
       "      <th>辽宁</th>\n",
       "      <th>重庆</th>\n",
       "      <th>陕西</th>\n",
       "      <th>青海</th>\n",
       "      <th>黑龙江</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>百货</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>造纸</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>旅游景点</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>超市连锁</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>铅锌</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "area       上海   云南   内蒙   北京   吉林   四川   天津   宁夏   安徽   山东 ...    湖南   甘肃  \\\n",
       "industry                                                   ...              \n",
       "百货        6.0  NaN  NaN  5.0  1.0  1.0  1.0  1.0  2.0  2.0 ...   2.0  2.0   \n",
       "造纸        1.0  NaN  NaN  NaN  NaN  1.0  NaN  1.0  1.0  5.0 ...   1.0  NaN   \n",
       "旅游景点      NaN  2.0  NaN  NaN  1.0  1.0  NaN  NaN  2.0  NaN ...   1.0  NaN   \n",
       "超市连锁      1.0  NaN  NaN  1.0  NaN  1.0  NaN  NaN  NaN  1.0 ...   1.0  NaN   \n",
       "铅锌        NaN  2.0  2.0  2.0  NaN  1.0  NaN  NaN  NaN  NaN ...   1.0  NaN   \n",
       "\n",
       "area       福建   西藏  贵州   辽宁   重庆   陕西  青海  黑龙江  \n",
       "industry                                        \n",
       "百货        1.0  NaN NaN  4.0  1.0  2.0 NaN  1.0  \n",
       "造纸        1.0  NaN NaN  NaN  NaN  NaN NaN  1.0  \n",
       "旅游景点      NaN  1.0 NaN  NaN  NaN  NaN NaN  NaN  \n",
       "超市连锁      2.0  NaN NaN  NaN  NaN  NaN NaN  NaN  \n",
       "铅锌        NaN  2.0 NaN  1.0  1.0  NaN NaN  NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e=d.sort_values(by=df.area.unique().tolist())\n",
    "e.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f086051198>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 管道方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f08ae28b70>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "df \\\n",
    ".pipe(lambda x:x.loc[:,['industry','area','pe']]) \\\n",
    ".pipe(lambda x:x.groupby(['industry','area']).count()) \\\n",
    ".pipe(lambda x:x.reset_index().pivot('industry','area','pe').sort_values(by=df.loc[:,'area'].unique().tolist())) \\\n",
    ".pipe(lambda x:sns.heatmap(x))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
